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Journal articles on the topic 'Road network pattern'
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1
Wang, Shiguang, Dexin Yu, Mei-Po Kwan, Huxing Zhou, Yongxing Li, and Hongzhi Miao. "The Evolution and Growth Patterns of the Road Network in a Medium-Sized Developing City: A Historical Investigation of Changchun, China, from 1912 to 2017." Sustainability 11, no. 19 (September 26, 2019): 5307. http://dx.doi.org/10.3390/su11195307.
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Understanding the evolution and growth patterns of urban road networks helps to design an efficient and sustainable transport network. The paper proposed a general study framework and analytical workflow based on network theory that could be applied to almost any city to analyze the temporal evolution of road networks. The main tasks follow three steps: vector road network drawing, topology graph generation, and measure classification. Considering data availability and the limitations of existing studies, we took Changchun, China, a middle-sized developing city that is seldom reported in existing studies, as the study area. The research results of Changchun (1912–2017) show the road networks sprawled and densified over time, and the evolution patterns depend on the historical periods and urban planning modes. The evolution of network scales exhibits significant correlation; the population in the city is well correlated with the total road length and car ownership. Each network index also presents specific rules. All road networks are small-world networks, and the arterial roads have been consistent over time; however, the core area changes within the adjacent range but is generally far from the old city. More importantly, we found the correlation between structure and function of the urban road networks in terms of the temporal evolution. However, the temporal evolution pattern shows the correlation varies over time or planning modes, which had not been reported
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Yang, Weiping. "AUTOMATIC CONSTRUCTION OF HIERARCHICAL ROAD NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 37–44. http://dx.doi.org/10.5194/isprsannals-iii-2-37-2016.
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This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.
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Yang, Weiping. "AUTOMATIC CONSTRUCTION OF HIERARCHICAL ROAD NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 37–44. http://dx.doi.org/10.5194/isprs-annals-iii-2-37-2016.
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This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.
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Wang, Bin, Xiaoxia Pan, Yilei Li, Jinfang Sheng, Jun Long, Ben Lu, and Faiza Riaz Khawaja. "Road network link prediction model based on subgraph pattern." International Journal of Modern Physics C 31, no. 06 (April 14, 2020): 2050083. http://dx.doi.org/10.1142/s0129183120500837.
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Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.
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Liu, Yan, Siqin Wang, Xuanming Fu, and Bin Xie. "A network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia." Environment and Planning A: Economy and Space 51, no. 2 (October 30, 2018): 279–82. http://dx.doi.org/10.1177/0308518x18810531.
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The severe loss of human life and material damage caused by traffic accidents is a growing concern faced by many countries across the world. In Australia, despite a decline in the total number of traffic collisions since 2001, the number of hit-parked-vehicle (HPV) collisions as a special type of road accident has increased over time. Utilizing the road collisions and roadway network data in Brisbane, Australia over a 10-year period from 2001 to 2010, we generated graphics illustrating the spatial patterning of high-risk road segments for HPV crashes identified using the local indicator of network-constrained clusters (LINCS) approach. These spatial patterns vary by days of the week and times of the day. Roads with high risk for HPV collision tend to occur in high-density road networks and cluster around road intersections. The methodology applied in this work is applicable to other network-constrained point-pattern analysis.
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SUN, ZHUO, JIANFENG ZHENG, and HONGTAO HU. "FRACTAL PATTERN IN SPATIAL STRUCTURE OF URBAN ROAD NETWORKS." International Journal of Modern Physics B 26, no. 30 (October 7, 2012): 1250172. http://dx.doi.org/10.1142/s021797921250172x.
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In this paper, we investigate the fractal pattern in spatial structure of urban road networks. By introducing sub-domain, an improved box-counting algorithm is proposed to obtain the fractal pattern. Numerical experiments explore the realistic urban road network at Dalian city in China. In order to clearly show the performance of our proposed box-counting algorithm, two other measures for urban road networks, i.e., density pattern and accessibility pattern are introduced, compared and discussed.
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van Nes, Akkelies. "The Impact of the Ring Roads on the Location Pattern of Shops in Town and City Centres. A Space Syntax Approach." Sustainability 13, no. 7 (April 1, 2021): 3927. http://dx.doi.org/10.3390/su13073927.
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This contribution demonstrates how inner ring roads change the location pattern of shops in urban areas with the application of the space syntax method. A market rational behaviour persists, in that shop owners always search for an optimal location to reach as many customers as possible. If the accessibility to this optimal location is affected by changes in a city’s road and street structure, it will affect the location pattern of shops. Initially, case studies of inner ring road projects in Birmingham, Coventry, Wolverhampton, Bristol, Tampere, and Mannheim show how their realisation affect the spatial structure of the street network of these cities and the location pattern of shops. The results of the spatial integration analyses of the street and road network are discussed with reference to changes in land-use before and after the implementation of ring roads, and current space syntax theories. As the results show, how an inner ring road is connected to and the type of the street network it is imposed upon dictates the resulting location pattern of shops. Shops locate and relocate themselves along the most spatially-integrated streets. Evidence on how new road projects influence the location pattern of shops in urban centres are useful for planning sustainable city centres.
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Yang, Chao, and Qi Liu. "Road Network Pattern Classification Using GEV Distribution Parameters." International Journal of Engineering and Manufacturing 2, no. 3 (June 29, 2012): 21–29. http://dx.doi.org/10.5815/ijem.2012.03.04.
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Sreelekha, M. G., K. Krishnamurthy, and M. V. L. R. Anjaneyulu. "Interaction between Road Network Connectivity and Spatial Pattern." Procedia Technology 24 (2016): 131–39. http://dx.doi.org/10.1016/j.protcy.2016.05.019.
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Gudi, Ganga, and Dr Hanumanthappa M. "Traffic Flow Pattern in Road Network Using Clustering." Volume 5 - 2020, Issue 8 - August 5, no. 8 (August 19, 2020): 229–30. http://dx.doi.org/10.38124/ijisrt20aug206.
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Wireless communication has become important in location-based services. The enormous amount of data is extracted for useful information to solve the real world problem. Global positioning system, is used to captures the position of an object at specific time period. The scheme is finding the congested route by considering the number of vehicles in a road segment. It consists of two methods, firstly it finds the group of points based on consistency of route points and second it arranges the groups in sequence of values for each route
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Zhang, Jianchen, and Heying Li. "Incremental Updating Information Extraction and Topology Conflict Detection Method for Updating Road Network." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-429-2019.
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<p><strong>Abstract.</strong> In recent years, Located Based Service (LBS) has become one of the hotspots on the application of geographic information to the government and the public. Providing a good performance map to the users is an important part of the location service system. Compared with the traditional web spatial information service system, service system of map information also put forward higher requirements of real time and veracity. The problem of updating electronic map in real-time cannot be effectively solved, hindering the updating of multi-scale electronic map and restricting the development of location service market. As a very important branch for the updating data field of geographic information science, incremental update has become a hot issue in the current international GIS research field. The extraction of incremental information includes two key technologies, namely road network matching and road network selection. After incremental updating roads were inserted into the database, coordinating topological conflicts caused by these inserted roads was the mainly task. However, the accuracy and algorithm efficiency of each technology need further improvement to provide the users with the latest and the most accurate basic geographic data. As the "skeleton" element of a map, the road network rapidly changes, carrying out more urgent requires for the incremental road update. Aiming at key technology problems of the updated road network, the paper focused on the following three parts:</p><p>(1) To address the problems that the traditional probabilistic relaxation method only adopted geometric constraints as one of road matching criterions and could not respond to M: N matching pattern, an improved probabilistic relaxation method was proposed from the combined views of local optimization and global one, integrating geometric indicators with topology ones to achieve an effect with local optimization, as well as identifying M: N matching pattern by inserting virtual nodes to achieve a globally optimal effect. Then the matching strategies and corresponding implement algorisms were designed for different matching patterns. The case test showed that the overall matching accuracy of each evaluation indictor reached over 90%, increasing by 6%&ndash;12%; the evaluation indicators on both spatial and attribute properties increased by 4%&ndash;6%; the proper buffer threshold could be defined as twice the average value of the closest distances from all nodes in the candidate matching dataset.</p><p>(2) Aiming at the low accuracy and irrational structural selection caused by only using linear pattern or areal pattern, an improved method was proposed combining linear method as well as areal ones. The proposed method improves the Stroke generation algorithm of linear pattern using OLS model taking the overall information from the roads to be connected. Meanwhile, it partitioned road network by weighted Voronoi diagrams. The roads were selected under the process of calculating the importance of Stroke and its road density threshold. The test demonstrated that the improved method added the accuracy of Stroke generation results, and the results of road selection could maintain the overall characteristics of the road network as well as its spatial distribution information. Compared with method only using one pattern, the combined method had a better accuracy of road selection.</p><p>(3) According to the characteristics of detection topology for an updated road network, this paper proposed a topology conflict detection algorithm considering the incremental update of multi-scale network. The algorithm designed K-level topological neighbourhood to identify incremental neighbourhood road segments, built a topological refinement model based on geometric metrics, proposed checking rules based on comprehensive operation operator, and detected the conflict topology by using improved topological distance. The experimental results showed that 1) the accuracy and recall of the proposed method were more than 90%; 2) considering conflict topology caused by the generalization, the accuracy increased by 29.2%; 3) the average path length of a road network could be used as the reference of the best K-value in the K order incremental neighbourhood method.</p>
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Tettamanti, Tamás, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos, and István Varga. "PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK." Transport 33, no. 4 (December 5, 2018): 959–70. http://dx.doi.org/10.3846/16484142.2017.1352027.
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A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.
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Ranjan, Navin, Sovit Bhandari, Pervez Khan, Youn-Sik Hong, and Hoon Kim. "Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder." Sustainability 13, no. 9 (May 2, 2021): 5108. http://dx.doi.org/10.3390/su13095108.
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The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.
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Miao, Pan, Wang, Chen, Yan, and Liu. "Research on Urban Ecological Network Under the Threat of Road Networks—A Case Study of Wuhan." ISPRS International Journal of Geo-Information 8, no. 8 (July 31, 2019): 342. http://dx.doi.org/10.3390/ijgi8080342.
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The creation of a road network can lead to the fragmentation and reduction of the connectivity of the ecological habitat. The study of urban ecological networks under threat from rapidly developing road networks is of great significance in understanding the changes in urban ecological processes and in constructing a reasonable ecological network. Spatial syntax is a linear space analysis method based on graph theory. Taking Wuhan city as an example and adopting spatial syntax to quantify road network threat factors, two resistance surfaces are established based on land use type assignment and overlapping road network threat factor assignment. The ecological environment under two scenarios is constructed by combining the MSPA (Morphological Spatial Pattern Analysis) method and MCR (Minimal Cumulative Resistance) model to comprehensively evaluate the network. Results demonstrate that spatial syntax can effectively describe the spatial characteristics of the road network. The average resistance value of the study area increases by 15.94%, the length of corridor increases by 37.9 km, the energy consumption of biological and material exchanges increases, and the resistance increases. To a certain extent, the model reflects the impact of road network threats on ecological processes. The results are useful in identifying the impact of human activities on ecological processes and provide a reference point for the construction of urban ecological security patterns.
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Silva, Fabrício, Luciano José Minette, Amaury Paulo de Souza, Ângelo Casali de Moraes, and Stanley Schettino. "CLASSIFICATION OF FOREST ROADS AND DETERMINATION OF ROUTE USING GEOGRAPHIC INFORMATION SYSTEM." Revista Árvore 40, no. 2 (April 2016): 329–35. http://dx.doi.org/10.1590/0100-67622016000200015.
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ABSTRACT Geographic Information System (GIS) is an indispensable software tool in forest planning. In forestry transportation, GIS can manage the data on the road network and solve some problems in transportation, such as route planning. Therefore, the aim of this study was to determine the pattern of the road network and define transport routes using GIS technology. The present research was conducted in a forestry company in the state of Minas Gerais, Brazil. The criteria used to classify the pattern of forest roads were horizontal and vertical geometry, and pavement type. In order to determine transport routes, a data Analysis Model Network was created in ArcGIS using an Extension Network Analyst, allowing finding a route shorter in distance and faster. The results showed a predominance of horizontal geometry classes average (3) and bad (4), indicating presence of winding roads. In the case of vertical geometry criterion, the class of highly mountainous relief (4) possessed the greatest extent of roads. Regarding the type of pavement, the occurrence of secondary coating was higher (75%), followed by primary coating (20%) and asphalt pavement (5%). The best route was the one that allowed the transport vehicle travel in a higher specific speed as a function of road pattern found in the study.
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Cui, Xiaojie, Jiayao Wang, Fang Wu, Jinghan Li, Xianyong Gong, Yao Zhao, and Ruoxin Zhu. "Extracting Main Center Pattern from Road Networks Using Density-Based Clustering with Fuzzy Neighborhood." ISPRS International Journal of Geo-Information 8, no. 5 (May 21, 2019): 238. http://dx.doi.org/10.3390/ijgi8050238.
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The spatial pattern is a kind of typical structural knowledge that reflects the distribution characteristics of object groups. As an important semantic pattern of road networks, the city center is significant to urban analysis, cartographic generalization and spatial data matching. Previous studies mainly focus on the topological centrality calculation of road network graphs, and pay less attention to the delineation of main centers. Therefore, this study proposes an automatic recognition method of main center pattern in road networks. We firstly extract the main clusters from road nodes by improving the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with fuzzy set theory. Moreover, the center area is generated with road meshes according to the area ratio with the covering discs of the main clusters. This proposed algorithm is applied to the road networks of a monocentric city and polycentric city respectively. The results show that our method is effective for identifying the main center pattern in the road networks. Furthermore, the contrast experiments demonstrate our method’s higher accuracy.
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Shi, Ge, Jie Shan, Liang Ding, Peng Ye, Yang Li, and Nan Jiang. "Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City." International Journal of Environmental Research and Public Health 16, no. 13 (June 30, 2019): 2318. http://dx.doi.org/10.3390/ijerph16132318.
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Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban expansion and have significant influences on human activities, the environment, and socioeconomic development. Understanding the urban road network expansion pattern and its corresponding social and environmental effects is a reasonable way to optimize comprehensive urban planning and keep the city sustainable. This paper analyzes the spatiotemporal dynamics of urban road growth and uses spatial statistic models to describe its spatial patterns in rapid developing cities through a case study of Nanjing, China. A kernel density estimation model is used to describe the spatiotemporal distribution patterns of the road network. A geographically weighted regression (GWR) is applied to generate the social and environmental variance influenced by the urban road network expansion. The results reveal that the distribution of the road network shows a morphological character of two horizontal and one vertical concentration lines. From 2012 to 2016, the density of the urban road network increased significantly and developed some obvious focus centers. The development of the urban road network had a strong correlation with socioeconomic and environmental factors, which however, influenced it at different degrees in different districts. This study enhances the understanding of the effects of socio-economic and environmental factors on urban road network expansion, a significant indicator of urban expansion, in different circumstances. The study will provide useful understanding and knowledge to planning departments and other decision makers to maintain sustainable development.
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Stückelberger, Jürg, Hans Rudolf Heinimann, and Woodam Chung. "Improved road network design models with the consideration of various link patterns and road design elements." Canadian Journal of Forest Research 37, no. 11 (November 2007): 2281–98. http://dx.doi.org/10.1139/x07-036.
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The success of an automatic road network layout over steep terrain mainly depends on the model design. Most previous models have used a grid representation that considers only eight adjacent cells when evaluating feasible road links. Here, we present improved models and alignment constraints mapped on a mathematical graph for better designs that are more applicable under field conditions. We have refined the link pattern by considering up to 48 neighbouring cells and have introduced 16 directional classes per grid cell. Optimization techniques, such as shortest path, minimum spanning tree, and Steiner minimum tree algorithms, are used on the graph to derive a road network that is optimal in terms of its construction costs. These improved models have been applied to different mountainous project areas. Our results show that, by considering various link patterns and alignment constraints, one can determine more appropriate and cost-effective locations for road networks, especially in steep terrain.
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Li, Hao, Maosheng Hu, and Youxin Huang. "Automatic Identification of Overpass Structures: A Method of Deep Learning." ISPRS International Journal of Geo-Information 8, no. 9 (September 18, 2019): 421. http://dx.doi.org/10.3390/ijgi8090421.
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The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns.
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Farrokhi, Saeedeh, Roknoddin Eftekhari, Mehdi Pourtaheri, and Jalal Karami. "Analysis of Structural Pattern of Road Access Network in Villages Exporting Agricultural Crops (Case Study: Maragheh City)." Journal of Sustainable Rural Development 4, no. 1 (September 23, 2019): 3–22. http://dx.doi.org/10.32598/jsrd.03.02.01.
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Purpose: Networks are studied and analyzed in two structural and functional aspects. Within this framework, the structural characteristics of networks are the result of their physical representation and independence of their use. Hence, the structural analysis of the road network enables us to generate semantic information (concerning village dynamics) and enrich useful spatial data. The topology or geometrical characteristics of the network can be analyzed through some algorithms of graph theory. The present research was conducted to analyze the structural pattern of rural road access network based on apple crop and export relationships in Maragheh. Methods: Research data were collected from 43 sample villages and 12 cold stores of agricultural crops (located in some villages) receiving apple crops from other villages. Data were analyzed using the network analysis method through UCINET.6 software. Results: The results showed that the access network in the study area had a star network pattern and among the studied villages, Gol village had a higher concentration compared to other villages. Conclusion: Concentration of storage equipment and storage of agricultural products such as cold storage in some villages and also the relatively good condition of access roads to these villages has increased the centrality of the above villages and the formation of a star pattern in the access network of the region. This has led to reduced access and isolation of villages far from the center of the network and thus reduced the incentive to produce in these villages. Therefore, considering the effect that the type of road network model has on the ease of access, distance and cost of villagers' access to services, especially services related to the type of rural products such as warehouses and cold storages of agricultural products, Therefore, it is recommended to provide a link among the villages through clustering to facilitate the access of villagers to the cold stores and save their cost, time, and distance.
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Bosurgi, Gaetano, Orazio Pellegrino, and Giuseppe Sollazzo. "Road Functional Classification Using Pattern Recognition Techniques." Baltic Journal of Road and Bridge Engineering 14, no. 3 (September 26, 2019): 360–83. http://dx.doi.org/10.7250/bjrbe.2019-14.448.
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The existing international standards suggest a methodology to assign a specific functional class to a road, by the values of some features, both geometrical and use-related. Sometimes, these characteristics are in contrast with each other and direct the analyst towards conflicting classes for a road or, worse, one or more of these features vary heterogeneously along the road. In these conditions, the analyst assigns the class that, by his capability and experience, he retains the most appropriate, in a very subjective way. On the contrary, the definition of an automatic procedure assuring an objective identification of the most appropriate functional class for each road would be desirable. Such a solution would be useful, especially when the road belongs to the existing infrastructure network or when it was not realised by out of date standards. The proposed procedure regards the definition of a classification model based on Pattern Recognition techniques, considering 13 input variables that, depending on their assumed value, direct the analyst towards one of the four functional classes defined by the Italian standards. In this way, it is possible to classify a road even when its characteristics are heterogeneous and conflicting. Moreover, the authors analysed the model limitations, in terms of errors and dataset size, considering observation and variable numbers. This approach, representing a beneficial decision support tool for the decision-maker, is exploitable for both planned and existing roads and becomes particularly advantageous for road agencies aiming to optimally allocate their limited funds for specific interventions assuring the achievement of a fixed functional class.
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Zeng, Qian, Ren, Xu, and Wei. "Road Landscape Morphology of Valley City Blocks under the Concept of “Open Block”—Taking Lanzhou City as an Example." Sustainability 11, no. 22 (November 7, 2019): 6258. http://dx.doi.org/10.3390/su11226258.
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The unique valley geographical environment and the congestion-prone road landscape make valley city traffic jammed easily. In this paper, under the background of “open blocks”, two open patterns, which correspond to two different road landscapes ("ideal grid opening" and "open under realistic conditions"), are proposed. Taking Lanzhou city as an example, six basic statistical characteristics are used to compare and analyze the changes of road network topology in blocks to find out which open pattern is more suitable for valley cities. The results show that the pattern "open under realistic conditions" has a significant effect on the improvement of network performance and capacity. Specifically, breaking the "large blocks" and developing the small-scale blocks help to alleviate the traffic pressure. Besides, the opening of blocks located along river valley has a more positive effect on improving road network performance than the blocks sited in the inner area of cities.
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He and Cao. "Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions." Sustainability 11, no. 17 (August 29, 2019): 4724. http://dx.doi.org/10.3390/su11174724.
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With the in-depth implementation of the “Belt and Road” initiative (BRI), the investment patterns between Belt and Road countries have also become more complicated. The impact of this complex investment network on regional economic development is also growing. To reveal the complexity of this investment pattern, and to better promote the sustainable development of the region’s economy, this paper used the complex network method to study the foreign direct investment(FDI) network of 50 countries along the Belt and Road from 2003 to 2017, revealing its structural and behavioral characteristics and evolution process. The results showed that the imbalance of the investment network structure is outstanding, and preferential selection behavior is obvious. The Central and Eastern European countries show significant clustering behavior. In addition, the network evolved slowly and followed the “Pareto rule” in the early stages of its evolution. The BRI was a turning point in the evolution process. On this basis, the quadratic assignment procedure (QAP) regression analysis method was used to further study the factors affecting the formation process of this investment pattern. It found that economic development level, geographical distance, and bilateral trade were the main influencing factors. Among them, bilateral trade had the greatest impact on the pattern of network.
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Zhang, Zhengxin, and Yunhong Wang. "JointNet: A Common Neural Network for Road and Building Extraction." Remote Sensing 11, no. 6 (March 22, 2019): 696. http://dx.doi.org/10.3390/rs11060696.
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Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neural network to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.
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He, Ming Guang, Yuan Hua Jia, and Hua Nan Li. "Evaluation Model of Road Network Vulnerability and its Genetic Algorithm Solution." Applied Mechanics and Materials 587-589 (July 2014): 1749–52. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.1749.
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Road network vulnerability is the loss estimate of multiple-link failure in road network cause by unplanned incidents. To evaluate road network vulnerability, the research adopted the improved Beckmann and FIM composite pattern. Then it carries out a solution based on Genetic Algorithm to quantify the road network vulnerability by defining the condition of combination road sections (open or closed) in road network which can find the critical links and nodes in the road network at the same time. The research turns out to be helpful both on road network planning and planning process of resource allocation for mitigation and recovery under disruption (take the snow disaster in southern China for instance).
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Han, Cui, Miao, Wang, and Chen. "Identifying Spatial Patterns of Retail Stores in Road Network Structure." Sustainability 11, no. 17 (August 21, 2019): 4539. http://dx.doi.org/10.3390/su11174539.
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Understanding the spatial patterns of retail stores in urban areas contributes to effective urban planning and business administration. A variety of methods have been proposed in the scientific literature to identify the spatial patterns of retail stores. These methods invariably employ arbitrary grid cells or administrative units (e.g., census tracts) as the fundamental analysis units. As most urban retail stores are distributed along street networks, using area-based analysis units is subject to statistical biases and may obfuscate the spatial pattern to some extent. Using the street segment as the analysis unit, this paper derives the spatial patterns of retail stores by crawling points of interest (POI) data in Zhengzhou, a city in central China. Then, the paper performs the network-based kernel density estimation (NKDE) and employs several network metrics, including the global, local, and weighted closeness centrality. Additionally, the paper discusses the correlation between the NKDE value and the closeness centrality across different store types. Further analysis indicates that stores with a high correlation tend to be distributed in city centers and subnetwork centers. The comparison between NKDE and cell-based KDE shows that our proposed method can address potential statistical issues induced by the area-based unit analysis. Our finding can help stakeholders better understand the spatial patterns and trends of small business expansion in urban areas and provide strategies for sustainable planning and development.
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Noori, Fatemeh, Hamid Kamangir, Scott A. King, Alaa Sheta, Mohammad Pashaei, and Abbas SheikhMohammadZadeh. "A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder." ISPRS International Journal of Geo-Information 9, no. 7 (July 20, 2020): 456. http://dx.doi.org/10.3390/ijgi9070456.
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In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure.
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Amjad Qtaishat, Deaa Al-Deen, Abd Al Azez Hdoush, and Eng Loiy Qasim Alzu’Bi. "Development of the Road Network in the City of Salt in 2004 and 2016 Using GIS." Modern Applied Science 13, no. 10 (September 19, 2019): 94. http://dx.doi.org/10.5539/mas.v13n10p94.
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The aim of this study is to analyze the structure of the road network in As-Salt City in the period between 2004 and 2016, in order to identify the road employability in terms of the degree of connectivity, rotation, accessibility, and density. The relationship between the social properties and road distribution are also examined through analysis of the network characteristics concerning population distribution. The data used in this study was based on the As-Salt City Municipality Database supported with fieldwork done in 2016. The network analysis approach using GIS was used to calculate the roads employability.
The study compares between the results of the analysis using the cognitive model of the road network for the years 2004 and 2016, knowing that the number of nodes in 2004 and 2016 was constant indicating the number of neighborhoods is 20, while the number of links changed from 42 links in 2004 to 50 links in 2016 and the average center of roads was determined, and it was estimated that the average road center is located near the municipality of As-Salt
The study indicates that the road network suffers from a low degree of communication and rotation and the standard distance of road sites in the study area. The standard distance for each group was 2338.49 m. There is a disparity in the distribution of road network within As-Salt City, and the proportion of roads lengths dose not suit the population distribution pattern. The neighborhood of Al- Salalem, includes 19.5% of the total number of roads in As-Salt, because the neighborhood of Al-Salalem contains the highest population census and this is accompanied by urban growth, which is necessarily accompanied by the presence of roads. Therefore, it is recommended to have a plan to redistribute the population in the city and to establish new roads to reduce the problems of traffic in the city.
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Joksimovic, Dusica, Michiel C. J. Bliemer, and Piet H. L. Bovy. "Optimal Toll Design Problem in Dynamic Traffic Networks with Joint Route and Departure Time Choice." Transportation Research Record: Journal of the Transportation Research Board 1923, no. 1 (January 2005): 61–72. http://dx.doi.org/10.1177/0361198105192300107.
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Road pricing is one of the market-based traffic control measures that can influence travel behavior to alleviate congestion on roads. This paper addresses the effects of uniform (constant, fixed) and time-varying (step) tolls on the travel behavior of users on the road network. The problem of determining optimal prices in a dynamic traffic network is considered by applying second-best tolling scenarios imposing tolls only to a subset of links on the network and considering elastic demand. The optimal toll design problem is formulated as a bilevel optimization problem with the road authority (on the upper level) setting the tolls and the travelers (on the lower level) who respond by changing their travel decisions (route and departure time choice). To formulate the optimal toll design problem, the so-called mathematical program with equilibrium constraints (MPEC) formulation was used, considering the dynamic nature of traffic flows on the one hand and dynamic pricing on the other. Until now, the MPEC formulation has been applied in static cases only. The model structure comprises three interrelated levels: (a) dynamic network loading, (b) route choice and departure time choice, and (c) road pricing level. For solving the optimal toll design problem in dynamic networks, a simple search algorithm is used to determine the optimal toll pattern leading to optimization of the objective function of the road authority subject to dynamic traffic assignment constraints. Nevertheless, uniform and time-varying pricing is analyzed, and a small hypothetical network is considered.
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Orłowski, Grzegorz. "Spatial distribution and seasonal pattern in road mortality of the common toad Bufo bufo in an agricultural landscape of south-western Poland." Amphibia-Reptilia 28, no. 1 (2007): 25–31. http://dx.doi.org/10.1163/156853807779799045.
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AbstractAmphibians are the group of animals suffering particularly from the presence of roads and vehicle traffic. The seasonal migration to breeding places undertaken by amphibians in the temperate climate zone is the main reason for their appearance on roads. Between June 2001 and August 2003, 957 common toads Bufo bufo were recorded killed on 48.8 km road network with various traffic volumes (350-10500 cars per 24 h), situated in the agricultural landscape of south-western Poland. The highest mortality was recorded in April (57% of all road-kills). The places with highest recorded mortality varied markedly throughout the year. In spring, many more animals died within the built-up areas, while in summer and autumn their number increased in the open countryside. During the whole study period, 73% of all road-kills were recorded on roads (55% of all controlled) with the lowest traffic volume (350-470 cars per 24 h). The average number of road-kills on roads with the high traffic volume (5700-10500 cars per 24 h) was over 15 times lower than on the roads with low traffic (0-0.17 road-kills per 100 m on roads with high traffic vs 2.59 road-kills per 100 m on roads with low traffic). The number of road-kills on 15 road sections was most closely related to the abundance of local populations of Bufo bufo and to the size of water bodies situated in the road vicinity. The yearly level of local mortality in breeding populations of Bufo bufo due to the vehicle traffic ranged from 2 to 18%.
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Li, P., Y. Li, J. Feng, Z. Ma, and X. Li. "AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 113–17. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-113-2020.
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Abstract. Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this paper, a segmentation-shape analysis based method is proposed to detect road intersections and their branch directions from an image. In the region of interest, it uses the contour shape of the segmented-intersection area to form a feature vector representing its geometric information. The extracted feature vector is then matched with some template vectors in order to find the best matched intersection pattern, obtain the type of intersection and the direction of connected roads. The experimental analysis are carried out with ISPRS Vaihingen and Toronto images. The experimental results show that the proposed method can extract most of the road intersections correctly. For the Vaihingen image, the the completeness and correctness are 81% and 87%, respectfully, while for the Toronto image, the the completeness and correctness are 78% and 85%, respectfully. It can help to build more correct and complete road network.
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Onuigbo, I. C., T. Adewuyi, J. O. Odumosu, and G. A. Oluibukun. "Applications of Surveying and Geoinformatics for Planning New Routes to Solve Traffic Congestion in part of Minna Metropolis (Kpakungu, a case study)." March 2019 3, no. 1 (March 2019): 149–60. http://dx.doi.org/10.36263/nijest.2019.01.0105.
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The volume of traffic generated by land-use pattern varies during different periods of the day but there is usually a predictable pattern of such traffic volumes. Most often, the structure of urban land-use fails to provide easy and convenient traffic movement, which in the case of the study area is usually that of vehicles and pedestrian traffic. The fact is that Minna is presently experiencing rapid urban growth. Both the authorities and citizens seem to simply ignore this and its impact on human existence. The research is based on Road Traffic Network Analysis in Minna, to develop a road network map and determine the causes of Traffic Congestion in Kpakungu specifically. Quickbird satellite imagery was used in analyzing and mapping out the existing road network within the study area. Field survey aspects involving measuring of roads, traffic count, coordinates captured were also undertaken. It was discovered that the causes of the traffic pressure in the study area was as a result of the relocation of Federal University of Technology, Minna to its permanent site in Gidan Kwanu and the relocation of National Examination Council(NECO) Headquarter. Majority of the traffic pressure in the area were as a result of vehicles coming from Maikunkele, Bosso, Maitumbi, Minna central, Dutsen Kura, Chanchaga, Tunga, Sahuka-kahuta and BarikinSale going to Bida, Gidan-Kwanu or NECO office. It was concluded that alternative roads should be provided for vehicle diversion to limit the congestion of traffic on the road.
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Priambodo, Bagus, Azlina Ahmad, and Rabiah Abdul Kadir. "Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 01 (January 21, 2020): 18. http://dx.doi.org/10.3991/ijoe.v16i01.11671.
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For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments. Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.
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Fachrie, Noor, and Iwan Rudiarto. "Kajian Variabel Penentu Peningkatan Status Jalan Nasional di Lintas Selatan Jawa Barat." JURNAL PEMBANGUNAN WILAYAH & KOTA 12, no. 1 (March 10, 2016): 73. http://dx.doi.org/10.14710/pwk.v12i1.11458.
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The Indonesian archipelago that stretches from Sabang( Sumatra Islands ) to Merauke ( Papua Islands ) has a variety of abundant natural resources , one of which are West Java Province . Central Government through the Ministry of Public Works – Public Housing has allocated Rp 1 trillion in APBN 2013 (presidential directive allocation) for the road construction of south traffic in West Java. This thesis aims to assess the effect / assessment of variables (i) land use - transportation; (ii) the pattern of development of the area and the road network system; (iii) spatial and regions; and (iv) the level of transport in the National Transportation System (SISTRANAS) towards improving the status of south traffic road of West Java to be became a national road in perspective the function of the road network, which is expected to provide an objective assessment in evaluating the proposed of roads status improvement. Positivistic and rationalistic approach used in this study to design variables and criteria for assessment of land use; the pattern of development of the area and the road network system; spatial and regions; as well as the transport level in the National Transportation System (SISTRANAS). Quantitative descriptive method in this research is intended to provide a description / overview and assessment of data analysis in the form of numbers. The research concludes that the variable of land use - transport and national transportation system (Sistranas) variable can be taken into consideration, because these two variables more realistic in assessing improvement the status and function of the national road compared to the other variables.
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Wen, Zhengmin, Zhenqiang Li, Wenshuo Luo, Yuxin Fu, Juan Quan, and Guohua Zhou. "Discussion on the Sustainable Development Pattern of Road Network Structure of Cities with a Population of 1-2 Million in China." E3S Web of Conferences 237 (2021): 04018. http://dx.doi.org/10.1051/e3sconf/202123704018.
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The urban road network system is the main carrier of urban traffic. The constraint factor that urban road network has on traffic leads to major urban traffic problems. In this paper, a questionnaire survey and the Delphi method are determined to determine the case cities with a population of 1-2 million at home and abroad. Literature research and comparative, analytical, and inductive research methods are used to compare, analyse, and evaluate the advantages and disadvantages of the road network structure of the case cities, to summarize the development law of the urban road network, and to propose a sustainable development structure model for each development stage of urban road network: the mode made of ring freeway + trunk street (the embryonic stage), the mode made of ring freeway + expressway + trunk street (the incubation stage), the mode made of outer ring freeway + outer ring expressway and radial expressway + trunk street (the mature stage). The innovative point of this paper is to put forward a sustainable development pattern for the road network structure of cities with a population of 1-2 million in China.
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ZHU, Ji-shuang, and Ning ZHANG. "Modeling Road Network Capacity and Service Level under Variable Demand Pattern." Systems Engineering - Theory & Practice 28, no. 6 (June 2008): 170–76. http://dx.doi.org/10.1016/s1874-8651(09)60027-2.
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Kostin, Vitalii, and Lena Halounová. "AN ANALYSIS OF SPATIAL STRUCTURE OF URBAN REGIONAL NETWORKS USING GIS." Acta Polytechnica 59, no. 1 (February 28, 2019): 35–41. http://dx.doi.org/10.14311/ap.2019.59.0035.
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Road network is the foundation of the urbanization process. Initially, it represented an indispensable part not only for the very existence of cities but also for their further economic and social development. Gradually evolving over an extended period of time, networks acquire a certain pattern that can affect the functioning of the entire urban system. This article presents an analysis of the structural properties of the transportation networks across the thirteen large urbanized regions in the Czech Republic. Taking advantage of modern GIS technologies, we investigate the geometric and topological characteristics of road networks on detailed spatial data. The aim of this study is to analyse the qualities of transportation networks that arise from the interaction of their structural components. The results show that the properties of studied urban networks vary from region to region, however, we have determined some common patterns.
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Tian, Jing, Mengting Yu, Chang Ren, and Yingzhe Lei. "Network-scape metric analysis: a new approach for the pattern analysis of urban road networks." International Journal of Geographical Information Science 33, no. 3 (November 18, 2018): 537–66. http://dx.doi.org/10.1080/13658816.2018.1545234.
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Sahitya, K. Sai, and Csrk Prasad. "GIS-Based Urban Road Network Accessibility Modeling Using MLR, ANN and ANFIS Methods." Transport and Telecommunication Journal 22, no. 1 (January 29, 2021): 15–28. http://dx.doi.org/10.2478/ttj-2021-0002.
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Abstract A sustainable transportation system is possible only through an efficient evaluation of transportation network performance. The efficiency of the transport network structure is analyzed in terms of its connectivity, accessibility, network development, and spatial pattern. This study primarily aims to propose a methodology for modeling the accessibility based on the structural parameters of the urban road network. Accessibility depends on the arrangement of the urban road network structure. The influence of the structural parameters on the accessibility is modeled using Multiple Linear Regression (MLR) analysis. The study attempts to introduce two methods of Artificial Intelligence (AI) namely Artificial Neural Networks (ANN) and Adaptive network-based neuro-fuzzy inference system (ANFIS) in modeling the urban road network accessibility. The study also focuses on comparing the results obtained from MLR, ANN and ANFIS modeling techniques in predicting the accessibility. The results of the study present that the structural parameters of the road network have a considerable impact on accessibility. ANFIS method has shown the best performance in modeling the road network accessibility with a MAPE value of 0.287%. The present study adopted Geographical Information Systems (GIS) to quantify, extract and analyze different features of the urban transportation network structure. The combination of GIS, ANN, and ANFIS help in improved decision-making. The results of the study may be used by transportation planning authorities to implement better planning practices in order to improve accessibility.
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Zhang, Qi, Byung Doo Jung, and Young In Kwon. "Model for a New Traffic Information System Based on Inter-Vehicle Communication Ad Hoc." Applied Mechanics and Materials 145 (December 2011): 325–29. http://dx.doi.org/10.4028/www.scientific.net/amm.145.325.
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This paper studies and explores an innovative pattern for a new traffic information system based on Inter-Vehicle Communication Ad Hoc. The main characteristic of this pattern is that the functions for traffic information collection and transmission are realized with the use of the moving vehicle which integrates the average speed of the vehicle on the current road section with the average speed transmitted by other vehicles in the Ad Hoc through inter-vehicle communication and with no need for using fixed facilities for traffic information collection and transmission. The traffic information of the current road section within certain period can be transmitted by the moving vehicles. Because the moving vehicle constantly changes its Ad Hoc network and the vehicles traffic information can be transmitted interactively, the traffic information of every road section can quickly spread through the whole road network. In view of this pattern, this paper establishes relative math models of automatic collection and transmission, based on Ad Hoc, of the traffic information.
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Karpinski, Mikolaj, Svitlana Kuznichenko, Nadiia Kazakova, Oleksii Fraze-Frazenko, and Daniel Jancarczyk. "Geospatial Assessment of the Territorial Road Network by Fractal Method." Future Internet 12, no. 11 (November 17, 2020): 201. http://dx.doi.org/10.3390/fi12110201.
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This paper proposes an approach to the geospatial assessment of a territorial road network based on the fractals theory. This approach allows us to obtain quantitative values of spatial complexity for any transport network and, in contrast to the classical indicators of the transport provisions of a territory (Botcher, Henkel, Engel, Goltz, Uspensky, etc.), consider only the complexity level of the network itself, regardless of the area of the territory. The degree of complexity is measured by a fractal dimension. A method for calculating the fractal dimension based on a combination of box counting and GIS analysis is proposed. We created a geoprocessing script tool for the GIS software system ESRI ArcGIS 10.7, and a study of the spatial pattern of the transport network of the Ukraine territory, and other countries of the world, was made. The results of the study will help to better understand the different aspects of the development of transport networks, their changes over time and the impact on the socioeconomic indicators of urban development.
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Su, Fei, Honghui Dong, Limin Jia, Zhao Tian, and Xuan Sun. "Space–time correlation analysis of traffic flow on road network." International Journal of Modern Physics B 31, no. 05 (February 9, 2017): 1750027. http://dx.doi.org/10.1142/s0217979217500278.
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Space–time correlation analysis has become a basic and critical work in the research on road traffic congestion. It plays an important role in improving traffic management quality. The aim of this research is to examine the space–time correlation of road networks to determine likely requirements for building a suitable space–time traffic model. In this paper, it is carried out using traffic flow data collected on Beijing’s road network. In the framework, the space–time autocorrelation function (ST-ACF) is introduced as global measure, and cross-correlation function (CCF) as local measure to reveal the change mechanism of space–time correlation. Through the use of both measures, the correlation is found to be dynamic and heterogeneous in space and time. The finding of seasonal pattern present in space–time correlation provides a theoretical assumption for traffic forecasting. Besides, combined with Simpson’s rule, the CCF is also applied to finding the critical sections in the road network, and the experiments prove that it is feasible in computability, rationality and practicality.
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Li, Songjiang, Wen An, and Peng Wang. "Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 7 (December 20, 2020): 900–907. http://dx.doi.org/10.20965/jaciii.2020.p0900.
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The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.
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Zhao, Fangxia, Huijun Sun, Jianjun Wu, Ziyou Gao, and Ronghui Liu. "Analysis of Road Network Pattern Considering Population Distribution and Central Business District." PLOS ONE 11, no. 3 (March 16, 2016): e0151676. http://dx.doi.org/10.1371/journal.pone.0151676.
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Salman, Sinan, and Suzan Alaswad. "Alleviating road network congestion: Traffic pattern optimization using Markov chain traffic assignment." Computers & Operations Research 99 (November 2018): 191–205. http://dx.doi.org/10.1016/j.cor.2018.06.015.
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Cai, Ying Feng, Hai Wang, and Wei Gong Zhang. "Learning Patterns of Motion Trajectories Using Real-Time Tracking." Advanced Materials Research 403-408 (November 2011): 2768–71. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2768.
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The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Experiments in actual road scene show the effectiveness of the proposed algorithm.
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Gui, Qinchang, Chengliang Liu, and DeBin Du. "The Structure and Dynamic of Scientific Collaboration Network among Countries along the Belt and Road." Sustainability 11, no. 19 (September 22, 2019): 5187. http://dx.doi.org/10.3390/su11195187.
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Although a number of studies have discussed the economic, geopolitical and environmental impacts of the Belt and Road Initiative (BRI), there is a scarcity of analysis on the importance of science in the Belt and Road (B&R). Adopting bibliographical data from Clarivate Analytics’ Web of Science database for the period 2000–2018, this study investigates the network properties, topological structure, spatial pattern, position of countries, core-periphery sets, and the hierarchy of the network from a dynamic perspective. The results show that scientific collaboration is increasingly frequent. The “hub-and-spoke” and triangulated structures coexist, shaping the landscape of the network. With the decline of Central and Eastern Europe, and the rise of the Asia-Pacific region, the spatial pattern evolves from ‘‘strong Western and weak Eastern” to ‘‘weak Western and strong Eastern’’. The central position has been occupied by India, China, and Turkey, while Russia’s influence has lessened over time. Moreover, the collaboration network is a typical core–periphery structure with prominent hierarchical features. China, Poland, and Saudi Arabia are the top-tier coordination centers within sub-networks. Finally, this study provides policy recommendations and prospective research directions.
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Tian, Zhao, Li-Min Jia, Hong-Hui Dong, Zun-Dong Zhang, and Yang-Dong Ye. "Fuzzy peak hour for urban road traffic network." Modern Physics Letters B 29, no. 15 (June 10, 2015): 1550074. http://dx.doi.org/10.1142/s0217984915500748.
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Traffic congestion is now nearly ubiquitous in many urban areas and frequently occurs during rush hour periods. Rush hour avoidance is an effective way to ease traffic congestion. It is significant to calculate the rush hour for alleviating traffic congestion. This paper provides a method to calculate the fuzzy peak hour of the urban traffic network considering the flow, speed and occupancy. The process of calculation is based on betweenness centrality of network theory, optimal separation method, time period weighting, probability–possibility transformations and trapezoidal approximations of fuzzy numbers. The fuzzy peak hour of the urban road traffic network (URTN) is a trapezoidal fuzzy number [m1, m2, m3, m4]. It helps us (i) to confirm a more detailed traffic condition at each moment, (ii) to distinguish the five traffic states of the traffic network in one day, (iii) to analyze the characteristic of appearance and disappearance processes of the each traffic state and (iv) to find out the time pattern of residents travel in one city.
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Silva, Cristiano, Lucas Silva, Leonardo Santos, João Sarubbi, and Andreas Pitsillides. "Broadening Understanding on Managing the Communication Infrastructure in Vehicular Networks: Customizing the Coverage Using the Delta Network." Future Internet 11, no. 1 (December 20, 2018): 1. http://dx.doi.org/10.3390/fi11010001.
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Over the past few decades, the growth of the urban population has been remarkable. Nowadays, 50% of the population lives in urban areas, and forecasts point that by 2050 this number will reach 70%. Today, 64% of all travel made is within urban environments and the total amount of urban kilometers traveled is expected to triple by 2050. Thus, seeking novel solutions for urban mobility becomes paramount for 21st century society. In this work, we discuss the performance of vehicular networks. We consider the metric Delta Network. The Delta Network characterizes the connectivity of the vehicular network through the percentage of travel time in which vehicles are connected to roadside units. This article reviews the concept of the Delta Network and extends its study through the presentation of a general heuristic based on the definition of scores to identify the areas of the road network that should receive coverage. After defining the general heuristic, we show how small changes in the score computation can generate very distinct (and interesting) patterns of coverage, each one suited to a given scenario. In order to exemplify such behavior, we propose three deployment strategies based on simply changing the computation of scores. We compare the proposed strategies to the intuitive strategy of allocating communication units at the most popular zones of the road network. Experiments show that the strategies derived from the general heuristic provide higher coverage than the intuitive strategy when using the same number of communication devices. Moreover, the resulting pattern of coverage is very interesting, with roadside units deployed a circle pattern around the traffic epicenter.
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Krigas, Nikos, Maria A. Tsiafouli, Georgios Katsoulis, Nefta-Eleftheria Votsi, and Mark van Kleunen. "Investigating the Invasion Pattern of the Alien Plant Solanum elaeagnifolium Cav. (Silverleaf Nightshade): Environmental and Human-Induced Drivers." Plants 10, no. 4 (April 20, 2021): 805. http://dx.doi.org/10.3390/plants10040805.
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Invasive alien plant species have impacts on nature conservation, ecosystem services and agricultural production. To identify environmental and human-related drivers of the invasion of Solanum elaeagnifolium (Solanaceae)—one of the worst alien invasive plants worldwide—we conducted an extensive drive-by survey across the Greek territory (presence/absence data; all national major multilane highways; 12–25% of the remaining road network; driven 3–5 times during 2000–2020). These data were linked in GIS with (i) physical environmental attributes (elevation, climate, soil properties) and (ii) type and intensity of human-related activities (land uses, settlements and road type). Compared to previous records, our survey showed that the range of S. elaeagnifolium increased by 1750% during the last decades, doubling its main distribution centers and reaching higher elevations. Our study revealed that the presence of S. elaeagnifolium is associated with (i) higher maximum temperatures and precipitation in summer and low precipitation in winter, as well as with (ii) soil disturbance related to agricultural activities, settlements and road networks, thus facilitating its spread mainly at low altitudes. Our study elucidates the current invasion pattern of S. elaeagnifolium and highlights the urgent need for its widespread monitoring, at least in the noninvaded areas in Greece that have been surveyed in this study. Preventative measures and integrative initiatives should be implemented quickly, and urgently incorporated into current agricultural, road network and conservation-management regimes.